Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

TL;DR

The paper introduces ModuLand, a fast and accurate integrative method for detecting overlapping network modules, revealing hierarchical structures, key nodes, and predicting network dynamics in complex networks.

Contribution

It presents a novel influence function-based landscape approach that unifies and extends existing modularization methods for comprehensive network analysis.

Findings

Determines high-resolution overlapping modules.

Uncovers detailed hierarchical network structures.

Identifies key nodes and predicts network dynamics.

Abstract

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.